Hydrologic evaluation of CH-2018 climate scenarios using a stochastic high-resolution downscaling approach

N. Peleg, S. Fatichi, P. Molnar, P. BurlandoInstitute of Environmental EngineeringETH Zürich

Coordination Meeting Hydro-CH201810.5.2017, Bern-Ittigen

Rationale• new CH2018 scenarios will provide more detail in climate change

signals than CH2011• broader scope of CC impact assessment requires methods to assess

CC impacts that account for higher space-time resolutions

1. High space (sub-km) and time (sub-daily) resolutions are necessary for runoff predictions in Alpine catchments

2. Distributed and physically-based hydrological modelling is necessary for hydrological consistency (soil water content, ET, snow, etc.)

3. It is important to partition uncertainty in climate change impact studies, in particular include internal climate variability

Total Unc. Climate Model Unc. Stochastic Unc. Scenario Unc.

ΔP

r [m

m]

Historical Period – Stochastic Unc.

Historical Period:

1981-2010

ΔP

r [m

m]

Partitioning uncertainty

Role of internal climate variability is important for climate change impact studies, especially for precipitation-driven processes

Fatichi et al. (2016, EF)

High resolution downscaling approach

WHY• Quantile mapping: one CC trajectory is not enough• Internal (stochastic) climate variability is important for CC impacts

HOW• Apply CH2018 to space-time statistics for future climate• Consider 3 emission scenarios• Generate spatial fields of precipitation, temperature,…• Apply to 3 basins: Maggia, Kl. Emme, Thur• Apply distributed physically-based model Topkapi-ETH• Analyse changes in hydrological variables

Task 1: Generating climate ensembles

1: Gauge and radar data for calibration

2: Stochastic realizations (n=50)

3: Validation of rain statistics on three study catchments (areas)

Task 2: Generating future climate

4: Multi-model ensemble5: Realizations (n=50)6: Partitioning uncertainty

Runs: 3 x 3 x 50 x 10 x 5 (#catchment-ensemble, #climate-ensembles, #realizations, #members, #decades)

4000 4050 4100 4150 42000

5

10

[mm

/h]

Precipitation

4000 4050 4100 4150 4200

10

20

30

[°C

]

Air Temperature

4000 4050 4100 4150 42000

500

1000

[W/m

2]

Solar Radiation

4000 4050 4100 4150 42000

0.5

1

[-]

Relative Humidity

4000 4050 4100 4150 42000

5

[m/s

]

Wind Speed

Hour

4000 4050 4100 4150 4200950

960

970

[mb

ar]

Hour

Atmospheric Pressure

What is AWE-GEN-2d ?

• Advanced WEather GENeratorfor a 2-dimensional grid AWE-GEN-2d (Peleg et al., 2017)

• High spatial resolution (2x2 km for precipitation & could cover, 100x100 m for air temperature, solar radiation, vapor pressure, atm. Pressure, and near-surface wind)

• High temporal resolution (1 hr)

Peleg et al. (2017)

What is AWE-GEN-2d ?

• 5-min (1 h) precipitation and cloud cover on a 2x2 km grid

• 1 hr air temperature, solar radiation, vapor pressure, atmospheric pressure and near-surface wind on a 100x100 m grid

Paschalis et al. (2013)

Incoming radiation [W m-2]

Jun 17th, 02:00 pm

Temperature [°C]

CH2018

Task 3: Hydrological impact analysis

• Distributed physically-based hydrological model Topkapi-ETH

• 3 catchments: Kl Emme, Maggia, Thur (landuse, regulation, natural)

• calibration-validation with current climate (30 yr period)

• future climate runs (moving 10 yrperiods)

• Analysis: mean changes (Q, ET, SWC, SM/IM, SC, etc.), peak flows, low flows; partitioning uncertainty

Fatichi et al. (2015)Paschalis et al. (2014)

What is new compared to CH2011 ?

1. internal climate variability2. fully-distributed approach3. additional hydrological variables

(e.g. snow cover, ET, …)

Addor et al. (2014, WRR)

1 2 3

Main references

Fatichi, S., S. Rimkus, P. Burlando, R. Bordoy, and P. Molnar (2015), High-resolution distributed analysis of climate and anthropogenic changes on the hydrology of an Alpine catchment, Journal of Hydrology, 525, 362-382.

Fatichi, S., V. Y. Ivanov, A. Paschalis, N. Peleg, P. Molnar, S. Rimkus, J. Kim, P., Burlando, and E. Caporali (2016), Uncertainty partition challenges the predictability of vital details of climate change, Earth’s Future, 4, 240–251.

Paschalis, A., P. Molnar, S. Fatichi, and P. Burlando (2013), A stochastic model for high-resolution space-time precipitation simulation, Water Resources Research, 49, 8400-8417.

Paschalis, A., S. Fatichi, P. Molnar, S. Rimkus, and P. Burlando (2014), On the effects of small scale space–time variability of rainfall on basin flood response, Journal of Hydrology, 514, 313-327.

Peleg, N., S. Fatichi, A. Paschalis, P. Molnar, and P. Burlando (2017), An advanced stochastic weather generator for simulating 2-d high resolution climate variables, Journal of Advances in Modeling Earth Systems, under review.